COL-864: Special Topics in Artificial Intelligence
Planning and Estimation for Autonomous Systems
Credits: (3-0-0)
Holi Term 2022
Description
Planning and estimation are central to autonomous systems operating in the real world. This course will cover the concepts, principles and methods for intelligent decision-making with imperfect or uncertain knowledge. Students will develop an understanding of how different planning and learning techniques are usefulin problem domains where robots or other embodied-AI agents are deployed. Introduction to Artificial Intelligence (COL333-671) or Introduction to Machine Learning (COL774 or equivalent). Programming proficiency and knowledge of probabilistic models, basic deep learning, basic search algorithms, logic and probability will be an advantage.
Announcements
- The first class will be held on Tuesday January 4, 2022 over MS Teams.
- Minor examination will be conducted on Thursday February 17, 2022. Information will be updated below.
- Assignment I is released on Friday February 11, 2022. Information appears below.
- Assignment II is released on Wednesday March 23, 2022. Information appears below.
- Major examination will be conducted on Wednesday April 06, 2022. Information will be updated below.
Course Information
- Instructor: Rohan Paul
- Classes: Slot AD
- Teaching Assistant: Vikas Upadhyay (vikas.upadhyay@cse.iitd.ac.in), Aadish Jain, Satyam Jay and Mohit Kataria
Lectures
| S. No. | Topic | Class Material |
|---|---|---|
| 1 | Course Organization | Slides |
| 2 | Course Introduction | Slides |
| 3 | Agent Representation - I | Slides |
| 4 | Planning Motions | Slides |
| 5 | State Space Planning | Slides |
| 6 | State Estimation - I | Slides |
| 7 | Agent Representation - II | Slides |
| 8 | State Estimation - II | Slides |
| 9 | Task Planning | Slides |
| 10 | Markov Decision Processes | Slides |
| 11 | Model-Based RL | Slides |
| 12 | Model-Free RL | Slides | 13 | DQNs and Policy Gradients | Slides | 14 | Partially-Obervable MDPs | Slides | 15 | Imitation Learning | Slides |
Assignments
Examination
- Information regarding minor examination will be provided here.
- Information regarding major examination will be provided here.
References
- [AIMA] Artificial intelligence: a modern approach. Russell, Stuart J., and Peter Norvig. Link.
- [PR] Probabilistic robotics. Thrun, Sebastian, Wolfram Burgard, and Dieter Fox. Link. Online.
- [DM] Mykel Kochenderfer,Decision Making Under Uncertainty
- [DL] Deep Learning. Ian Goodfellow, Yoshua Bengio and Aaron Courville. Online [DL]
- [PA] Planning Algorithms. LaValle, S.M., 2006. Cambridge University Press. Online.
- [SB] Reinforcement Learning (Second Edition). Richard Sutton and Andrew Barto. MIT Press. 2018. Online.
Background Reading Material
- Pointers for reviewing some of the background topics for the course. Some of the material may be briefly in class.
- Classical Planning (AIMA Ch. 3)
- Neural Networks (DL Ch. 6)
- Markov Decision Processes (AIMA Ch 17.1-17.3 )
- Probabilistic Models (AIMA Ch 14.1-14.5)
- These are starting pointers but not an exhaustive list, you are welcome to explore further.
Learning outcomes
At the end of the course students will be able to: model autonomous systems as AI agents, formulate/solve relevant planning/estimation tasks. Further, students will gain insights in the computational challenges arising from uncertainty and how to incorporate recent learning-based methods decision-making algorithms.